Opto Engineering®
Opto Engineering®
Optics basics
Image quality
Lens types
Light in machine vision
LED illumination
Illumination geometries and techniques
Wavelength and optical performance
Structured illumination
Illumination safety and class risks of LEDs
according to EN62471
Camera types
Sensor and camera features
Digital camera interfaces
Types of vision systems
How a vision system works
he basic purpose of a lens of any kind is to collect the light scattered by an object and recreate
an image of the object on a light-sensitive ‘sensor’ (usually CCD or CMOS based).
A certain number of parameters must be considered when choosing optics, depending on the
area that must be imaged (field of view), the thickness of the object or features of interest (depth
of field), the lens to object distance (working distance), the intensity of light, the optics type
(telecentric/entocentric/pericentric), etc.
The following list includes the fundamental parameters that must be evaluated in optics
Field of View (FoV): total area that can be viewed by the lens and imaged onto the camera
Working distance (WD): object to lens distance where the image is at its sharpest focus.
Depth of Field (DoF): maximum range where the object appears to be in acceptable focus.
Sensor size: size of the camera sensor’s active area. This can be easily calculated
by multiplying the pixel size by the sensor resolution (number of active pixels
in the x and y direction).
Magnification: ratio between sensor size and FoV.
Resolution: minimum distance between two points that can still be distinguished as separate
points. Resolution is a complex parameter, which depends primarily on the lens and camera
Optics basics
Lens approximations and equations
he main features of most optical systems can be calculated with a few parameters, provided that some approximation is accepted.
The paraxial approximation requires that only rays entering the optical system at small angles with respect to the optical axis
are taken into account. The thin lens approximation requires the lens thickness to be considerably smaller than the radii of curvature
of the lens surfaces: it is thus possible
to ignore optical effects due to the real
Working distance
thickness of the lenses and to simplify
ray tracing calculations. Furthermore,
assuming that both object and image
space are in the same medium (e.g. air),
we get the fundamental equation:
1/s’ – 1/s = 1/f
where s (s’ ) is the object (image) position
with respect to the lens, customarily
designated by a negative (positive) value,
and f is the focal length of the optical
system (cf. Fig. 1). The distance from the
object to the front lens is called working
distance, while the distance from the
rear lens to the sensor is called back
focal distance. Henceforth, we will be
presenting some useful concepts and
formulas based on this simplified model,
unless otherwise stated.
Fig. 1: Basic parameters of an optical system.
Camera mounts
ifferent mechanical mounting systems are used to connect a lens to a camera, ensuring both good focus and image stability.
The mount is defined by the mechanical depth of the mechanics (flange focal distance), along with its diameter and thread pitch
(if present). It’s important that the lens flange focal distance and the camera mount flange distance are exactly the same, or focusing
issues may arise. The presence of a threaded mechanism allows some adjustment to the back focal distance, if needed. For example,
in the Opto Engineering ® PCHI series lenses, the backfocal adjustment is needed to adjust the focus for a different field of view.
C-mount is the most common optics mount in the industrial
market. It is defined by a flange focal distance of 17.526 mm,
a diameter of 1” (25.4 mm) with 32 threads per inch.
CS-mount is a less popular and 5 mm shorter version of the
Cmount, with a flange focal distance of 12.526 mm.
A CS-mount camera presents various issues when used together
with C-mount optics, especially if the latter is designed to work
at a precise back focal distance.
17.526 mm
1” x 32 TPI
Fig. 2: C-mount mechanical layout.
12.526 mm
1” x 32 TPI
Fig. 3: CS-mount mechanical layout.
F-mount is a bayonet-style mount originally developed
by Nikon for its 35 mm format cameras, and is still found in
most of its digital SLR cameras. It is commonly used with bigger
sensors, e.g. full-frame or line-scan cameras.
Lenses can be easily swapped out thanks to the bayonet mount,
but no back focal adjustment is possible.
Mxx-mount are different types of camera mounts defined by
their diameter (e.g. M72, M42), thread pitch (e.g. 1 mm, 0.75 mm)
and flange focal distance. They are a common alternative to the
F-mount for larger sensors.
T-mount (T1 = M42x1.0; T2 = M42 x 0.75)
M58-mount (M58 x 0.75)
46.5 mm
M58 x 0.75
M72-mount (M72 x 0.75)
44 mm
M72 x 0.75
48 mm
Fig. 4: F-mount mechanical layout.
Fig. 5: Mxx mount mechanical layouts.
Each camera mount is more commonly used with certain camera sensor formats. The most typical sensor formats are listed below.
It is important to remember that these are not absolute values – i.e. two cameras listed with the same sensor format may differ
substantially from one another in terms of aspect ratio (even if they have the same sensor diagonal). For example, the Sony Pregius
IMX250 sensor is listed as 2/3” and has an active area of 8.45 mm x 7.07 mm. The CMOSIS CMV2000 sensor is also listed as 2/3”
format but has an active area of 11.26 mm x 5.98 mm.
2048 px x 10 µm
2048 px x 14 µm
4096 px x 7 µm
4096 px x 10 µm
7450 px x 4.7 µm
6144 px x 7 µm
8192 px x 7 µm
12288 px x 5 µm
20.5 mm
28.6 mm
28.6 mm
35 mm
41 mm
43 mm
57.3 mm
62 mm
Fig. 6: Common line scan sensors formats.
Sensor type
Full frame - 35 mm
Full frame - 35 mm
Fig. 7: Common area scan sensors format.
Fig. 8: Area scan sensors relative sizes.
Back focal length adjustment
any cameras are found not to respect the industrial standard for C-mount (17.52 mm), which defines the flange-to-detector
distance (flange focal length). Besides all the issues involved with mechanical inaccuracy, many manufacturers don’t take into
the due account the thickness of the detector’s protection glass which, no matter how thin, is still part of the actual flange to detector
This is why a spacer kit is supplied with Opto Engineering® telecentric lenses including instructions on how to tune the back focal
length at the optimal value.
Focal Length
he focal length of an optical system is a measure of how
strongly the system converges or diverges rays of light.
For common optical systems, it is the distance over which
collimated rays coming from infinity converge to a point.
If collimated rays converge to a physical point, the lens is said to
be positive (convex), whereas if rays diverge the focus point is
virtual and the lens is said to be negative (concave cf. Fig. 9).
All optics used in machine vision application are overall positive,
i.e. they focus incoming light onto the sensor plane.
Fig. 9: Positive (left) and negative (right) lens.
For optical systems used in machine vision, in which rays reflected from a faraway object are focused onto the sensor plane, the focal
length can be also seen as a measure of how much area is imaged on the sensor (Field of View): the longer the focal length, the smaller
the FoV and vice versa (this is not completely true for some particular optical systems, e.g. in astronomy and microscopy).
f = 8 mm
f = 25 mm
f = 50 mm
Fig. 10: Focal length and field of view.
Magnification and field of view
he magnification M of an optics describes the ratio between
image (h’) and object size (h):
M = h’/h
A useful relationship between working distance (s), magnification
(M) and focal length ( f ) is the following:
s = f (M-1)/M
Macro and telecentric lenses are designed to work at a distance
comparable to their focal length (finite conjugates), while
Fig. 11: Given a fixed sensor size, if magnification is increased
fixed focal length lenses are designed to image objects located
the field of view decreases and viceversa.
at a much greater distance than their focal length (infinite
conjugates). It is thus convenient to classify the first group by their magnification, which makes it easier to choose the proper lens given
the sensor and object size, and the latter by their focal length.
Since fixed focal length lenses also follow the previous equation, it is possible to calculate the required focal length given the
magnification and working distance, or the required working distance given the sensor size, field of view and focal length, etc.
(some examples are given at the end of this section). For macro and telecentric lenses instead, the working distance and magnification
are typically fixed.
F/# and depth of field
very optical system is characterized by an aperture stop, that
determines the amount of light that passes through it.
For a given aperture diameter d and focal length f we can
calculate the optics F-number:
Image sensor
F/# = f / d
Focal length f
Fig. 12: Aperture of an optical system.
Typical F-numbers are F/1.0, F/1.4, F/2, F/2.8, F/4, F/5.6, F/8, F/11, F/16, F/22 etc. Every increment in the F-number (smaller aperture)
reduces incoming light by a factor of 2.
The given definition of F-number applies to fixed focal length lenses where the object is located ‘at infinity’ (i.e. a distance much greater
than its focal length). For macro and telecentric lenses where objects are at closer distance, instead the working F/# (wF/#)is used.
This is defined as:
WF/# = (1 + M) • F/#
A common F-number value is F/8, since smaller apertures
could give rise to diffraction limitations, while lenses with
larger apertures are more affected by optical aberrations and
f 2.8
The F-number affects the optics depth of field (DoF), that is the
range between the nearest and farthest location where an object
is acceptably in focus. Depth of field is quite a misleading concept,
because physically there is one and only one plane in object
space that is conjugate to the sensor plane. However, being
mindful of diffraction, aberration and pixel size, we can define an
“acceptable focusing distance” from the image conjugate plane,
based on subjective criteria. For example, for a given lens, the
acceptable focusing distance for a precision gauging application
requiring a very sharp image is smaller than for a coarse visual
inspection application.
A rough estimate of the field depth of telecentric and macro
lenses (or fixed focal length lenses used in macro configuration) is
given by the following formula:
f 5.6
f 11
f 16
f 22
of field
Fig. 13: Relationship between aperture (F/#) and DoF.
DoF [mm] = WF/# • p [µm] • k / M2
where p is the sensor pixel size (in microns), M is the lens
magnification and k is a dimensionless parameter that depends
on the application (reasonable values are 0.008 for measurement
applications and 0.015 for defect inspection). For example, taking
p = 5.5 µm and k = 0.015, a lens with 0.25X mag and WF/# = 8 has
an approximate dof = 10.5 mm.
Fig. 14: Relationship between F/# amount of incoming ligth, resolution and DoF.
Image quality
hen designing a machine vision system, it is important to consider its performance limitations, in terms of optical parametes
(FOV, DoF, resolution), aberrations, distortion and mechanical features.
“Aberrations” is a general category including the principal factors that cause an optical system to perform differently than the ideal
case. There are a number of factors that do not allow a lens to achieve its theoretical performance.
Physical aberrations
he homogeneity of optical materials and surfaces is the
first requirement to achieve optimum focusing of light rays
and proper image formation. Obviously, homogeneity of real
materials has an upper limit determined by various factors
(e.g. material inclusions), some of which cannot be eliminated.
Dust and dirt are external factors that certainly degrade a lens
performance and should thus be avoided as much as possible.
Spherical aberration
Lens rays
pherical lenses (Fig. 15) are very common because they are
relatively easy to manufacture. However, the spherical shape
is not ideal for perfect imaging - in fact, collimated rays entering
the lens at different distances from the optical axis will converge
to different points, causing an overall loss of focus. Like many
optical aberrations, the blur effect increases towards the edge of
the lens.
Optical axis
Best focus point
To reduce the problem, aspherical lenses (Fig. 16) are often used
- their surface profile is not a portion of a sphere or cylinder,
but rather a more complex profile apt to minimize spherical
aberrations. An alternative solution is working at high F/#’s, so
that rays entering the lens far from the optical axis and causing
spherical aberration cannot reach the sensor.
Fig. 15: Lens with spherical aberration.
Lens rays
Optical axis
Best focus point
Fig. 16: Aspherical lens.
Chromatic aberration
he refractive index of a material is a number that describes
the scattering angle of light passing through it – essentially
how much rays are bent or refracted - and it is function of the
wavelength of light. As white light enters a lens, each wavelength
takes a slightly different path. This phenomenon is called
dispersion and produces the splitting of white light into its
spectral components, causing chromatic aberration.
The effect is minimal at the center of the optics, growing towards
the edges.
Chromatic aberration causes color fringes to appear across
the image, resulting in blurred edges that make it impossible to
correctly image object features.
While an achromatic doublet can be used to reduce this kind of
aberration, a simple solution when no color information is needed
is using monochrome light. Chromatic aberration can be of two
types: longitudinal (Fig. 17) and lateral (Fig. 18), depending on the
direction of incoming parallel rays.
RGB color rays
Optical axis
Best focus point
Fig. 17: Longitudinal/axial chromatic aberration.
RGB color rays
Optical axis
Best focus point
Fig. 18: Lateral /transverse chromatic aberration.
stigmatism (Fig. 19) is an optical aberration that occurs when
rays lying in two perpendicular planes on the optical axis
have different foci.
This causes blur in one direction that is absent in the other
direction. If we focus the sensor for the sagittal plane, we see
circles become ellipses in the tangential direction and vice versa.
Fig. 19: Astigmatism aberration.
oma aberration (Fig. 20) occurs when parallel rays entering
the lens at a certain angle are brought to focus at different
positions, depending on their distance from the optical axis.
A circle in the object plane will appear in the image as a cometshaped element, which gives the name to this particular
aberration effect.
Fig. 20: Coma aberration.
Field curvature
ield curvature aberration (Fig. 21) describes the fact that
parallel rays reaching the lens from different directions do not
focus on a plane, but rather on a curved surface.
This causes radial defocusing, i.e. for a given sensor sensor
position, only a circular crown will be in focus.
Fig. 21: Field curvature aberration.
ith a perfect lens, a squared element would only be
transformed in size, without affecting its geometric
properties. Conversely, a real lens always introduces some
geometric distortion, mostly radially symmetric (as a reflection
of the radial symmetry of the optics). This radial distortion can
be of two kinds: barrel and pincushion distortion. With barrel
distortion, image magnification decreases with the distance from
the optical axis, giving the apparent effect of the image being
wrapped around a sphere. With pincushion distortion image
magnification increases with the distance from the optical axis.
Lines that do not pass through the center of the image are bent
inwards, like the edges of a pincushion.
Fig. 22: Distortion.
What about distortion correction?
ince telecentric lenses are a real world object, they show some residual distortion which can affect measurement accuracy.
Distortion is calculated as the percent difference between the real and expected image height and can be approximated by a second
order polynomial.
If we define the radial distances from the image center as follows
Ra = actual radius
the distortion is computed as a function of Ra:
Re = expected radius
dist (Ra) = (Ra - Re)/Ra = c • Ra 2 + b • Ra + a
where a, b and c are constant values that define the distortion curve behavior; note that “a” is usually zero as the distortion is usually
zero at the image center. In some cases, a third order polynomial could be required to get a perfect fit of the curve.
In addition to radial distortion, also trapezoidal distortion must be taken into account. This effect can be thought of as the perspective
error due to the misalignment between optical and mechanical components, whose consequence is to transform parallel lines in object
space into convergent (or divergent) lines in image space.
Such effect, also known as “keystone” or “thin prism”, can be easily fixed by means of pretty common algorithms which compute the
point where convergent bundles of lines cross each other.
An interesting aspect is that radial and trapezoidal distortion are two completely different physical phenomena, hence they can be
mathematically corrected by means of two independent space transform functions which can also be applied subsequently.
An alternative (or additional) approach is to correct both distortions locally and at once: the image of a grid pattern is used to define
the distortion error amount and its orientation zone by zone. The final result is a vector field where each vector associated to a specific
image zone defines what correction has to be applied to the x,y coordinate measurements within the image range.
Why GREEN light is recommended for telecentric lenses?
ll lenses operating in the visible range, including OE Telecentric lenses, are achromatized through the whole VIS spectrum.
However, parameters related to the lens distortion and telecentricity are typically optimized for the wavelengths at the center
of the VIS range, that is green light. Moreover, the resolution tends to be better in the green light range, where the achromatization is
almost perfect.
“Green” is also better than “Red” because a shorter wavelength range increases the diffraction limit of the lens and the maximum
achievable resolution.
Contrast, resolution and diffraction
efects and optical aberrations, together with diffraction,
contribute to image quality degradation.
An efficient way to assess image quality is to calculate contrast,
that is the difference in luminance that makes an object - its
representation in the image or on a display - distinguishable.
Mathematically, contrast is defined as
C = [Imax – Imin ]/[ Imax + Imin ]
Fig. 23: Greyscale levels.
where Imax (Imin) is the highest (lowest) luminance.
In a digital image, ‘luminance’ is a value that goes from 0 (black) to a maximum value depending on color depth (number of bits used to
describe the brightness of each color). For typical 8-bit images (in grayscale, for the sake of simplicity), this value is 28 -1 = 255, since
this is the number of combinations (counting from the zero ‘black’ string) one can achieve with 8 bits sequences, assuming 0-1 values
for each.
Lens resolving power: transfer function
he image quality of an optical system is usually expressed by its transfer function (TF). TF describes the ability of a lens to resolve
features, correlating the spatial information in object space (usually expressed in line pair per millimeter) to the contrast achieved
in the image.
Periodic grating
Periodic grating
Fig. 24: Modulation and contrast transfer function.
What’s the difference between MTF (Modulation Transfer Function)
and CTF (Contrast Transfer Function)?
CTF expresses the lens contrast response when a “square pattern” (chessboard style) is imaged; this parameter is the most useful in
order to assess edge sharpness for measurement applications. On the other hand, MTF is the contrast response achieved when imaging
a sinusoidal pattern in which the grey levels range from 0 and 255; this value is more difficult to convert into any useful parameter for
machine vision applications. The resolution of a lens is typically expressed by its MTF (modulation transfer function), which shows the
response of the lens when a sinusoidal pattern is imaged.
w = 1/(2t)
For example, a black and white stripe pattern with 5 µm wide
stripes has a spatial frequency of 100 lp/mm.
The “cut-off frequency” is defined as the value w for which CTF is
zero, and it can be estimated as
TS diff. limit
TS 0.00 mm
TS 9.00 mm
TS 15.80 mm
TS 22.50 mm
Modulus of the OTF
However, the CTF (Contrast Transfer Function) is a more
interesting parameter, because it describes the lens contrast
when imaging a black and white stripe pattern, thus simulating
how the lens would image the edge of an object.
If t is the width of each stripe, the relative spatial frequency w will
wcut-off = 1/[WF/# • λ(mm)]
Spatial frequency in cycles per mm
For example, an Opto Engineering ® TC23036 lens (WF/#h F/8)
operating in green light (λ = 0.000587 mm) has a cut-off spatial
frequency of about
Fig. 25: MTF curves of TC23036 - green light.
wcut-off = [ 8 0.000587 mm ] = 210 lp/mm
Optics and sensor resolution
he cutoff spatial frequency is not an interesting parameter,
since machine vision systems cannot reliably resolve features
with very low contrast. It is thus convenient to choose a limit
frequency corresponding to 20% contrast.
A commonly accepted criterion to describe optical resolution
is the Rayleigh criterion, which is connected to the concept of
resolution limit. When a wave encounters an obstacle - e.g. it
passes through an aperture - diffraction occurs. Diffraction in
optics is a physical consequence of the wave-like nature of light,
resulting in interference effects that modify the intensity pattern
of the incoming wavefront.
Since every lens is characterized by an aperture stop, the image
quality will be affected by diffraction, depending on the lens
aperture: a dot-like object will be correctly imaged on the sensor
until its image reaches a limit size; anything smaller will appear to
have the same image – a disk with a certain diameter depending
on the lens F/# and on the light wavelength.
This circular area is called the Airy disk, having a radius of
rA = 1.22 λ f / d
where λ is the light wavelength, f is the lens focal length, d is the
aperture diameter and f /d is the lens F-number. This also applies
to distant objects that appear to be small.
If we consider two neighboring objects, their relative distance
can be considered the “object” that is subject to diffraction when
it is imaged by the lens. The idea is that the diffraction of both
objects’ images increases to the point that it is no longer possible
to see them as separate. As an example, we could calculate the
theoretical distance at which human eyes cannot distinguish that
a car’s lights are separated.
The Rayleigh’s criterion states that two objects are not
distinguishable when the peaks of their diffraction patterns are
closer than the radius of the Airy Disk rA (in image space).
Fig. 26: Airy disk separation and the Rayleigh criterion.
The Opto Engineering ® TC12120 telecentric lens, for example,
will not distinguish feature closer than
rA = 1.22 • 0.587 µm • 8 = 5.7 µm
in image space (e.g. on the sensor). The minimum resolvable size
in image space is always 2 rA , regardless of the real world size of
the object. Since the TC12120 lens has 0.052X magnification and
2rA = 11.4 µm, the minimum real-world size of the object that can
be resolved is 11.4 µm /0.052 = 220 µm.
For this reason, optics should be properly matched to the sensor
and vice versa: in the previous example, there is no advantage to
use a camera with 2 µm pixel size, since every “dot like” object will
always cover more than one pixel. In this case, a higher resolution
lens or a different sensor (with larger pixels) should be chosen.
On the other hand, a system can be limited by the pixel size,
where the optics would be able to “see” much smaller features.
The Transfer Function of the whole system should then be
considered, assessing the contribution from both the optics and
the sensor. It is important to remember that the actual resolution
limit is not only given by the lens F/# and the wavelength, but also
depends on the lens aberrations: hence, the real spatial frequency
to be taken into account is the one described by the MTF curves
of the desired lens.
Reflection, transmission and coatings
hen light encounters a surface, a fraction of the beam is reflected, another fraction is refracted (transmitted) and the rest is
absorbed by the material. In lens design, we must achieve the best transmission while minimizing reflection and absorption.
While absorption is usually negligible, reflection can be a real problem: the beam is in fact not only reflected when entering the lens
(air-glass boundary) but also when it exits the lens (glass-air). Let’s suppose that each surface reflects 3% of incoming light: in this case,
a two lenses system has an overall loss of 3*3*3*3 % = 81%. Optical coatings – one or more thin layers of material deposited on the lens
surface – are the typical solution: a few microns of material can dramatically improve image quality, lowering reflection and improving
Percent transmittance
Transmission depends considerably on the light wavelength:
different kind of glasses and coatings helps to improve
performance in particular spectral regions, e.g. UV or IR.
Generally, good transmission in the UV region is more difficult to
Commercial grade
fused quartz
Optical grade
fused quartz
Fused silica
750 1000
Wavelength, nanometers
Fig. 27: Percent transmittence of different kind of glasses.
Anti-reflective (AR) coatings are thin films applied to surfaces to
reduce their reflectivity through optical interference.
An AR coating typically consists of a carefully constructed stack
of thin layers with different refractive indices.
The internal reflections of these layers interfere with each
other so that a wave peak and a wave trough come together and
extinction occurs, leading to an overall reflectance that is lower
than that of the bare substrate surface.
Anti-reflection coatings are included on most refractive optics
and are used to maximize throughput and reduce ghosting.
Perhaps the simplest, most common anti-reflective coating
consists of a single layer of Magnesium Fluoride (MgF2), which has
a very low refractive index (approx. 1.38 at 550 nm).
Hard carbon anti-reflective HCAR coating: HCAR is an optical
coating commonly applied to Silicon and Germanium designed
to meet the needs of those applications where optical elements
are exposed to harsh environments, such as military vehicles and
outdoor thermal cameras.
This coating offers highly protective properties coupled with
good anti-reflective performance, protecting the outer optical
surfaces from high velocity airborne particles, seawater, engine
fuel and oils, high humidity, improper handling, etc.. It offers great
resistance to abrasion, salts, acids, alkalis, and oil.
ight that is focused on the sensor can be reduced by a number of internal factors, that do not depend on external factors.
Mount vignetting occurs when light is physically blocked on its
way to the sensor. Typically this happens when the lens image
circle (cross section of the cone of light projected by the lens) is
smaller than the sensor size, so that a number of pixels are not hit
by light, thus appearing black in the image. This can be avoided by
properly matching optics to sensors: for example, a typical 2/3”
sensor (8.45 x 7.07 mm, 3.45 µm pixel size) with 11 mm diagonal
would require a lens with a (minimum) image circle of 11 mm in
Aperture vignetting is connected to the optics F/#: a lens with a
higher F/# (narrower aperture) will receive the same light from
most directions, while a lens with a lower F/# will not receive the
same amount of light from wide angles, since light will be partially
blocked by the edges of the physical aperture.
Fig. 28: Example of an image showing vignetting.
Fig. 29: Lens with low F/# (left) and high F/# (right) seen from the optical axis (top)
and off-axis (button).
Light intensity
Cos4 vignetting describes the natural light falloff caused by light
rays reaching the sensor at an angle.
The light falloff is described by the cos^4(θ) function, where θ
is the angle of incoming light with respect to the optical axis in
image space.
The drop in intensity is more significant at wide incidence angles,
causing the image to appear brighter at the center and darker at
the edges.
Fig. 30: Cos4 vignetting. Light fall off coused by θ the angle with incoming light
with respect to the optical axis.
Lens types
any different types of optics are available in the industry, each tailored for different uses and applications. Below is a brief
overview of the most common lens types, along with their working principles and common applications.
elecentric lenses represent a special class of optics designed
to only collect collimated light ray bundles (i.e. parallel to the
optical axis, see Fig. 31), thus eliminating perspective errors.
Since only rays parallel to the optical axis are accepted, the
magnification of a telecentric lens is independent of the object
location. This unique feature makes telecentric lenses perfectly
suited for measurement applications, where perspective
errors and changes in magnification can lead to inconsistent
measurements. Because of its design, the front element of a
telecentric lens must be at least as large as the desired FOV,
making these lenses inadequate to image very large objects.
Parallel rays
Entrance pupil
The following drawings (Fig. 32) show the difference between
common optics (entocentric) and telecentric lenses. Fixed focal
length lenses are entocentric lenses, meaning that they collect
rays diverging from the optical axis. This allows them to cover
large FoVs but since magnification is different at different
working distances, these lenses are not suited to determine the
true dimensions of an object.
Fig. 31: Telecentric optics accepts only rays parallel to the optics axis.
Fig. 32: a) The design of a telecentric lens is such that objects at different distances
from the lens appear to have the same size.
Fig. 32: b) With entocentric optics, a change in the working distance is seen
on the sensor as perspective error.
Benefits of bi-telecentric lenses
Better Magnification Constancy
tandard telecentric lenses accept ray cones whose axis is parallel to the main optical axis; if the lens is only telecentric in object
space, ray cones passing through the optical system reach the detector from different angles depending upon the field position.
Moreover the optical wavefront is completely asymmetric since incoming telecentric rays become non-telecentric in image space.
As a consequence, the spots generated by ray cones on the detector plane change in shape and dimension from point to point in image
space (the point-spread function becomes non-symmetrical and a small circular spot grows larger and turns elliptical as you move from
the image center towards the borders).
Even worse, when the object is displaced, rays coming from a certain field point generate a spot that moves back and forth over the
image plane, thus causing a significant change in magnification. For this reason non bi-telecentric lenses show a lower magnification
constancy although their telecentricity might be very good if measured only in the object space.
non bi-telecentric
Fig. 33: (a) In a non image space telecentric lens (left) ray cones strike
the detector at different angles.
Bi-telecentric lenses are telecentric in both object and image space, which means that principal rays are parallel not only when entering
but also when exiting the lens.
This feature is essential to overcome all the accuracy issues concerned with mono-telecentric lenses such as point spread function
inhomogeneity and lack of magnification constancy through the field depth.
Fig. 33: (b) In a bi-telecentric lens (right) ray cones are parallel and reach the image
sensor in a way independent on the field position.
Increased field depth
ield depth is the maximum acceptable displacement of an
object from its best focus position. Beyond this limit the
image resolution becomes poor, because the rays coming from
the object can’t create sufficiently small spots on the detector:
blurring effect occurs because geometrical information carried
by the optical rays spread over too many image pixels.
Depth of field basically depends upon the optics F/#, which is
inversely proportional to the lens aperture diameter: the higher
the f-number the larger the field depth, with a quasi-linear
Increasing the F/# reduces ray cones divergence, allowing for
smaller spots to form onto the detector; however raising the F/#
over certain values introduces diffraction effects which limit the
maximum achievable resolution.
Bi-telecentricity is beneficial in maintaining a very good image
contrast even when looking at very thick objects (see Fig. 34):
the symmetry of the optical system and the rays parallelism help
the image spots with staying symmetrical, which reduces the blur
This results in a field depth being perceived as 20-30% larger
compared to non bi-telecentric optics.
Fig. 34: Image of a thick object viewed throughout its entire depth
by a bi-telecentric lens.
Even detector illumination
i-telecentric lenses boast a very even illumination of the
detector, which comes useful in several applications such as
LCD, textile and print quality control (Fig. 35).
When dichroic filters have to be integrated in the optical path
for photometric or radiometric measurements, bi-telecentricity
assures that the ray fan axis strikes the filter normal to its surface,
thus preserving the optical band-pass over the whole detector
Fig. 35: A bi-telecentric lens is interfaced with a tunable filter in order to perform
high resolution colour measurements. The image-side telecentricity ensures that the
optical bandpass is homogeneous over the entire filter surface and delivers an even
illumination of the detector, provided the object is evenly illuminated too.
How to choose the right telecentric lens
aving fixed working distance and aperture, telecentric lenses are classified by their magnification and image circle.
Choosing the right telecentric lens is easy: we must find the magnification under which the image fit the sensor.
Example. We need to measure the geometrical feature of a mechanical part (nut) using a telecentric lens and a 2048 x 2048, 5.5 µm sensor.
The nut is inscribed in a 10 mm diameter circle with 2 mm uncertainty on the sample position. What is the best choice?
Given the camera resolution and pixel size (2048 x 2048 pix, 5.5 µm), the sensor dimensions are calculated to be 11.26 x 11.26 mm.
The FOV must contain a 12 mm diameter circle, hence the minimum magnification required is 0.938X.
The Opto Engineering® TC23009 telecentric lens (M=1.000X, image circle 11 mm) would give a FOV of 11.26 mm x 11.26 mm,
but because of mechanical vignetting the actual FOV is only a 11 mm diameter circle. In this case, if a more accurate part placement
cannot be guaranteed, a lens with lower mag or a larger image circle must be chosen.
Using the Opto Engineering® TC2MHR016-x lens (M=0.767X, image circle 16.0 mm) we find a FOV of 14.68 x 14.68 mm which
is a very close match.
UV lens
cut-off frequency, UV
cut-off frequency, VIS
For example, the Opto Engineering ® TCUV series telecentric
lenses operate in the near UV range and deliver extremely high
resolution for very demanding measurement applications.
VIS lens
ince the diffraction limit allows higher resolution at shorter
wavelengths (see Fig. 36), UV optics can reach superior results
compared to standard lenses and can efficiently operate with
pixels as small as 1.75 µm.
Spacial frequency (line pairs/mm)
Fig. 36:The graph shows the limit performances (diffraction limit) of two lenses
operating at working F/# 8. The standard lens operates at 587 nm (green light)
while the UV lens operates at 365 nm.
Why Opto Engineering® telecentric lenses don’t integrate an iris?
Our TC lenses don’t feature an iris, but we can easily adjust the aperture upon request prior to shipping the lens, without any
additional costs or delays for the customer.
The reasons why our lenses don’t feature an iris are so many that the proper question would be “why other manufacturers integrate
• adding an iris makes a lens more expensive because of a feature that would only be used once or twice throughout
the product life
• iris insertion makes the mechanics less precise and the optical alignment much worse
• we would be unable to test the lenses at the same aperture that the customer would be using
• iris position is much less precise than a metal sheet aperture: this strongly affects telecentricity
• the iris geometry is polygonal, not circular: this changes the inclination of the main rays across the FOV, thus affecting the lens
distortion and resolution
• irises cannot be as well centered as fixed, round, diaphragms: proper centering is essential to ensure a good telecentricity
of the lens
• only a circular, fixed, aperture makes brightness the same for all lenses
• an adjustable iris is typically not flat and this causes uncertainty in the stop position,
which is crucial when using telecentric lenses!
• iris is a moving part that can be dangerous in most industrial environments. Vibrations could easily disassemble
the mechanics or change the lens aperture
• the iris setting can be accidentally changed by the user and that would change the original system configuration
• end users prefer having less options and only a few things that have to be tuned in a MV system
• apertures smaller than what is delivered by OE as a standard will not make sense as the resolution will decay because
of diffraction limit; on the other hand, much wider apertures would cause a reduction of the field depth.
The standard aperture of OE lenses is meant to optimize image resolution and field depth.
Why OE Telecentric lenses don’t feature a focusing mechanism?
As with the iris, a focusing mechanism would generate a
mechanical play in the moving part of the lens, thus making
it worse the centering of the optical system and also causing
trapezoidal distortion.
Another issue is concerned with radial distortion: the
distortion of a telecentric lens can be kept small only when
the distances between optical components are set at certain
values: displacing any element from the correct position would
increase the lens distortion. A focusing mechanism makes the
positioning of the lenses inside the optical system uncertain
and the distortion value unknown: the distortion would then
be different from the values obtained in our quality control
any machine vision applications require a complete view
of an object surface since many features to be inspected are
located on the object sides rather than on top.
Most cylindrical objects such as bottles and containers, as well as
many kinds of mechanical parts require an inspection of the side
surfaces to detect scratches and impurities or to read barcodes
or, again, to ensure that a label has been printed correctly.
In these cases, the most common approach is to use multiple
cameras (usually 3 or 4) in order to achieve several side views
of the part, in addition to the top view. This solution, besides
increasing the cost of the system, often creates a bottleneck in
the system performances, since the electronics or software must
process different images from different cameras simultaneously.
In other cases, vision engineers prefer to scan the outer surface
with line scan camera systems.
This approach also shows many technical and cost disadvantages:
the object must be mechanically rotated in the FOV which also
affects the inspection speed; moreover, line-scan cameras require
very powerful illumination. Also, the large size of linear detectors
increases the optical magnification of the system, thus reducing
field depth.
The 360° optics category encompasses different optical solutions
that capture rays diverging from the object (see Fig. 37), thus
imaging not only the object surface in front of the lens, but also
the object’s lateral surface (see optical diagram below).
The following images illustrate the working principle applied to
a pericentric lens (PC), a catadioptric lens (PCCD), a pinhole lens
(PCHI) and a boroscope lens (PCPB). Other 360° optical solutions
combine telecentric optics and mirror arrays, allowing you to
get a complete view of a sample with just one camera (TCCAGE,
PCPW and PCMP series).
Convergent rays
Fig. 37: Pericentric lens type. The entrance pupil is located in front of the lens.
Fig. 38: Opto Engineering® PC lens optical scheme,
sample image and unwrapped image.
Fig. 39: Opto Engineering ® PCCD optical scheme,
sample image and unwrapped image.
Fig. 40: Opto Engineering® PCHI optical scheme,
sample image and unwrapped image.
Fig. 41: Opto Engineering® PCPB optical scheme,
sample image and unwrapped image.
Fig. 42: Opto Engineering ® TCCAGE optical scheme and sample image.
Fig. 43: Opto Engineering ® PCPW: optical scheme and sample image.
Fig. 44: Opto Engineering ® PCMP: optical scheme and sample image.
acro lenses are fixed focal length lenses whose working distance is comparable to
their focal length. The recommended working distance from the object is usually
fixed, hence macro optics are usually described by their magnification.
Since macro lenses are specifically designed to image small and fixed FoVs, they tend
to have extremely low geometrical distortion. For example, the distortion of Opto
Engineering ® MC series lenses range from <0.05% to <0.01%.
ixed focal length lenses are
entocentric lenses, meaning that
they collect rays diverging from the
optical axis (see Fig. 45).
Fixed focal length lenses are commonly
used optics in machine vision, being
affordable products that are well suited
for standard applications.
Knowing the basic parameters - focal length and sensor size - it is
easy to calculate the field of view and working distance; the focus
can be adjusted from a minimum working distance to infinity;
usually also the iris is controlled mechanically, allowing you to
manually adjust the lens F/# and consequently the light intensity,
field depth and resolution.
Example. A ceramic tile (100 x 80 mm) must be inspected with
a fixed focal length lens from 200 mm away.
Which lens would you choose?
The Camera sensor has 2592 x 1944 res, with 2.2 µm pixels.
Recalling basic lens equations:
1/s’ (+)– 1/s (-) = 1/f(+)
M = h’/h = s’/ s
we find:
1/s ( h / h’ - 1 ) = 1/f
Diverging rays
WD = - s = - f ( h / h’ - 1 )
or consequently:
f = s / ( h / h’ - 1 )
and also
h = h’ ( 1 + s / f )
Fig. 45: Entocentric optics accept rays diverging from the lens.
Fixed focal length lenses are inexpensive and versatile, but they
are not suitable for all applications.
They usually introduce significant perspective errors and
geometric distortion that are incompatible with precision
measurement applications.
Also, the manually adjustable iris and focus introduce some
mechanical play, which makes these lenses not ideal for
applications requiring very consistent and repeatable settings.
keeping in mind that s and h’ (object position with respect to
the lens and image height) are customarily negative, while f
and h (focal length and object height) are customarily positive.
Also, in machine vision, we take h as the maximum value for
the desired field of view and h’ as the short side of the sensor,
to make sure the minimum requrested field of view is covered.
Given the sensor resolution and pixel size, we can calculate
the sensor dimensions. We set h’ = - 4.28 mm and h = 100 mm.
Hence, setting s = - 200 mm we find f = 8.2 mm.
With a standard 8 mm lens we would cover a slightly wider
FOV (137 x 102 mm).
Extension tubes
or most standard lenses the working distance (WD) is not a fixed parameter.
The focusing distance can be changed by adjusting a specific knob. Nevertheless,
there is always a minimum object distance (MOD) below which focusing becomes
impossible. Adding an extension tube (see Fig. 46) between the lens and the camera
increases the back focal length, making it possible to reduce the MOD.
This also increases the magnification of the lens or, in other words, reduces the FOV.
While very common in the vision industry, this procedure should be avoided as much as
possible, because it degrades the lens performance (resolution, distortion, aberrations,
brightness, etc.).
In these cases, it is recommended to use lenses natively designed to work at short
working distances (macro lenses).
Fig. 46: Extension tubes for fixed focal length lenses.
arifocal lenses are lenses with variable focal length, which can be adjusted by moving
groups of optical elements with respect to each other inside the lens. The variable
focal length allows for multiple combinations of working distances and magnifications,
offering several different configurations with a single lens.
Varifocal lenses, though, have the same reliability issues of fixed focal length lenses, plus
more uncertainty caused by the relative motion of lens groups inside the assembly.
oom lenses (parfocal lenses) are a special type of varifocal optics in which the
working distance is kept constant when changing focal length (i.e. focus is maintained
throughout the process). Actually, a zoom lens is generally defined as a lens that can
change magnification without changing its working distance: in this category, we can
also find macro zoom (e.g. Opto Engineering ® MCZR and MZMT) and telecentric zoom
lenses (Opto Engineering ® TCZR).
chempflug optics are a special class of lenses, either of the
fixed focal, macro or telecentric type, designed to meet the
Scheimpflug criterion.
Suppose that the object plane of an optical setup is not parallel to
the image plane (e.g. a camera-lens system imaging a flat target at
45°): this causes the image to be sharp only where the focus plane
and the target plane intersect.
Since the image and object planes are conjugated, tilting the
first plane by a certain angle will also cause the latter to tilt by a
corresponding angle. Once the focus plane is aligned to the target
plane, focus across the image is restored.
Sensor angle
The angle at which the sensor plane must be tilted is given by the
Scheimpflug criterion:
tan(θ’) = M • tan(θ)
θ’ = atan(M • tan(θ))
where M is the lens magnification, θ’ is the image plane tilt angle
(i.e. on the sensor side) and θ is the object plane tilt angle.
It is clear that at high magnifications this condition is impossible
to meet, since an object plane tilted by 45° would require to tilt
the sensor by 80°, causing severe mechanical and vignetting
issues (cf. Fig. 47, where M=5 black, M=1 blue, M=0.25 red).
Object angle
Fig. 47: Relationship between abject (θ) and sensor angle (θ’)
at different magnification M.
Image plane tilting is practically realized by changing the angle of
the camera with respect to the optics by means of special tiltable
mounts: the picture below illustrates an example of a Scheimpflug
telecentric setup.
Fig. 48: Example of Scheimpflug telecentric setup.
n machine vision, we find a number of interesting and high tech applications of IR radiation: the imaging process in some regions of the
spectrum requires specifically designed lenses called IR optics.
All objects with an absolute temperature over 0 K emit infrared (IR) radiation. Infrared radiant energy is determined by the
temperature and emissivity of an object and is characterized by wavelengths ranging from 0.76 µm (the red edge of the visible range)
to 1000 µm (beginning of microwaves range). The higher the temperature of an object, the higher the spectral radiant energy, or
emittance, at all wavelengths and the shorter the peak wavelength of the emissions. Due to limitations on detector range, IR radiation
is often divided into three smaller regions based on the response of various detectors.
SWIR (0.9-1.7 μm) is also called the «reflected infrared» region since radiation coming from a light source is reflected by the object in a
similar manner as in the visible range.
SWIR imaging requires some sort of illumination in order to image an object and can be performed only if some light, such as ambient
moon light or stars light is present. In fact the SWIR region is suitable for outdoor, night-time imaging. SWIR imaging lenses are
specifically designed, optimized, and anti-reflection coated for SWIR wavelenghts.
Indium Gallium Arsenide (InGaAs) sensors are the primary sensors used in SWIR, covering typical SWIR band, but can extend as low as
0.550 µm to as high as 2.5 µm.
A large number of applications that are difficult or impossible
to perform using visible light are possible using SWIR InGaAs
based cameras: nondestructive identification of materials, their
composition, coatings and other characteristics, Electronic
Board Inspection, Solar cell inspection, Identifying and Sorting,
Surveillance, Anti-Counterfeiting, Process Quality Control, etc...
When imaging in SWIR, water vapor, fog, and certain materials such
as silicon are transparent. Additionally, colors that appear almost
identical in the visible may be easily differentiated using SWIR.
MWIR (3-5 μm) and LWIR (8-14 μm) regions are also referred to as “thermal infrared” because radiation is emitted from the object
itself and no external light source is needed to image the object. Two major factors determine how bright an object appears to a
thermal imager: the object’s temperature and its emissivity (a physical property of materials that describes how efficiently it radiates).
As an object gets hotter, it radiates more energy and appears brighter to a thermal imaging system.
Atmospheric obscurants cause much less scattering in the MWIR and LWIR bands than in the SWIR band, so cameras sensitive to these
longer wavelengths are highly tolerant of smoke, dust and fog.
• MWIR collects the light in the 3 µm to 5 µm spectral band.
MWIR cameras are employed when the primary goal is
to obtain high-quality images rather than focusing on
temperature measurements and mobility. The MWIR band of
the spectrum is the region where the thermal contrast is higher
due to blackbody physics; while in the LWIR band there is quite
more radiation emitted from terrestrial objects compared
to the MWIR band, the amount of radiation varies less with
temperature: this is why MWIR images generally provide
better contrast than LWIR. For example, the emissive peak of
hot engines and exhaust gasses occurs in the MWIR band, so
these cameras are especially sensitive to vehicles and aircraft.
The main detector materials in the MWIR are InSb (Indium
antimonide) and HgCdTe (mercury cadmium telluride) also
referred to as MCT and partially lead selenide (PbSe).
• LWIR collects the light in the 8 µm to 14 µm spectral band
and is the wavelength range with the most available thermal
imaging cameras. In fact, according to Planck’s law, terrestrial
targets emit mainly in the LWIR. LWIR systems applications
include thermography/temperature control, predictive
maintenance, gas leak detection, imaging of scenes which span
a very wide temperature range (and require a broad dynamic
range), imaging through thick smoke, etc... The two most
commonly used materials for uncooled detectors in the LWIR
are amorphous silicon (a-Si) and vanadium oxide (VOx), while
cooled detectors in this region are mainly HgCdTe.
Athermalization. Any material is characterized by a certain temperature expansion coefficient and responds to temperature variations
by either increasing or decreasing its physical dimensions. Thus, thermal expansion of optical elements might alter a system’s optical
performance causing defocusing due to a change of temperature. An optical system is athermalized if its critical performance
parameters (such as Modulation Transfer Function, Back Focal Length, Effective Focal Length, …) do not change appreciably over the
operating temperature range.
Athermalization techniques can be either active or passive. Active athermalization involves motors or other active systems to
mechanically adjust the lens elements’ position, while passive athermalization makes use of design techniques aimed at compensating
for thermal defocusing, by combining suitably chosen lens materials and optical powers (optical compensation) or by using expansion
rods with very different thermal expansion coefficients that mechanically displace a lens element so that the system stays in focus
(mechanical compensation).
llumination is one of the most critical components of a machine vision system. The selection of
the appropriate lighting component for a specific application is very important to ensure that
a machine vision system performs its tasks consistently and reliably.
The main reason is that improper illumination results in loss of information which, in most cases,
cannot be recovered via software. This is why the selection of quality lighting components is of
primary importance: there is no software algorithm capable of revealing features that are not
correctly illuminated.
To make the most appropriate choice, one must consider many different parameters, including:
Lighting geometry
Light source type
Surface property of the material to be inspected or measured (e.g. color, reflectivity)
Item shape
Item speed (inline or offline application)
Mechanical constraints
Environment considerations
Since many parameters must be considered, the choice can be difficult and sometimes the wisest
advice is to perform feasibility studies with different light types to reveal the features of interest.
On the other hand, there are a number of simple rules and good practices that can help select the
proper lights and improve the image quality.
For every application, the main objectives are the following:
Maximizing the contrast of the features that must be inspected or measured
Minimizing the contrast of the features of no interest
Getting rid of unwanted variations caused by:
a. Ambient light
b. Differences between items that are non-relevant to the inspection task
Light in machine vision
n machine vision, light is mostly characterized by its wavelength,
which is generally expressed in nm (nanometers).
Light visible to the human eye has wavelengths in the range of
400-700 nm, between the infrared (with longer wavelengths)
and the ultraviolet (with shorter wavelengths): special
applications might require IR or UV light instead of visible light.
Basically light is electromagnetic radiation within a certain
portion of the electromagnetic spectrum (cf. Fig. 1): it can be
quasi-monochromatic (which means that it is characterized
by a narrow wavelength band, i.e. with a single color) or white
(distributed across the visible spectrum, i.e. it contains all colors).
Fig. 1: Electromagnetic specturm.
Basically, light interacts with materials (Fig. 2) by being
• Reflected and/or
• Transmitted and/or
• Absorbed
Additionally, when light travels across different media it refracts,
i.e. it changes direction. The amount of refraction is inversely
proportional to the light wavelength; i.e. violet light rays are bent
more than red ones.
This means that light with short wavelengths gets scattered more
easily than light with long wavelengths when hitting a surface
and is therefore, generally speaking, more suited for surface
inspection applications.
In fact, if we ideally consider wavelength as the only parameter
to be considered from the previous list, blue light is advised for
applications such as scratch inspection while longer wavelengths
such as red light are more suited for enhancing the silhouette of
transparent materials.
Fig. 2: Interaction of light with matter: reflection, adsorption and transmission.
LED illumination
LED lights are by far the most commonly
used in machine vision because they offer
a number of advantages, including:
• Fast response
• Suitable for pulse and strobe operations
• Mechanical resistance
• Longer lifetime, higher output stability
• Ease of creating various lighting
Quartz Halogen / Tungsten
Daytime sunlight
Relative intensity (%)
here are many different types of light
sources available (Fig. 3) including the
• Incandescent lamps
• Fluorescent lamps
• LED lights
Wavelength (nm)
Fig. 3: Emission spectra of different light sources.
Incandescent lamps are the well-known
glass bulbs filled with low pressure,
inert gas (usually argon) in which a thin
metal wire (tungsten) is heated to high
temperatures by passing an electric
current through it.
The glowing metal emits light on a broad
spectrum that goes from 400 nm up to
the IR. The result is a white, warm light
(corresponding to a temperature of 2870
K) with a significant amount of heat being
Fluorescent lamps are vacuum tubes
in which UV light is first produced (by
interaction between mercury vapor and
highly energetic electrons produced by
a cathode) and then is adsorbed by the
tube walls, coated with fluorescent and
phosphorescent material.
The walls then re-emit light over a
spectrum that again covers the whole
visible range, providing a “colder” white
light source.
LEDs (Light Emitting Diodes) produce
light via the annihilation of an electronhole pair in a positive/negative junction of
a semiconductor chip.
The light produced by an LED depends
on the materials used in the chip and is
characterized by a narrow spectrum, i.e. it
is quasi-monochromatic.
White light is produced as in the
fluorescent lamps, but the blue light
is absorbed and re-emitted in a broad
spectrum slightly peaked in the blue
LED power supply and output
Forward voltage vs. Forward current
One important consideration is that the luminous flux produced
by a single LED increases almost linearly with the current
while it does not do so with respect to the voltage applied:
1% uncertainty on the driving current will translate into 1%
luminance uncertainty, while 1% uncertainty on the input voltage
can result in a several percentage points variation (Fig. 4).
Forward current (mA)
n LED illuminator can be controlled by either setting the
voltage V across the circuit or by directly feeding the circuit
with electric current I.
For this reason, it is suggested to directly regulate the current
and not the voltage, so that the light output is stable, tightly
controlled and highly repeatable.
Forward voltage (V)
Forward current vs Relative luminous flux
Relative luminous flux (a.u.)
For example, in measurement applications, it is paramount to
obtain images with a stable grey level background to ensure
consistency of the results: this is achieved by avoiding light
flickering and ensuring that the LED forward current of the
telecentric light is precisely controlled: this is why Opto
Engineering ® LTLCHP telecentric illuminators feature builtin electronics designed to have less than 1‰ variation in LED
forward current intensity leading to very stable performances.
Forward current (mA)
Fig. 4: LED current, tension and light output graphs.
LED pulsing and strobing
EDs can be easily driven in a pulsed (on/off) regime and can be
switched on and off in sequence, turning them on only when
necessary. Usage of LEDs in pulsed mode has many advantages
including the extension of their lifespan.
If the LED driving current (or voltage) is set to the nominal value
declared by the LED manufacturer for continuous mode, we talk
about pulsed mode: the LED is simply switched on and off.
LEDs can also be driven at higher intensities (i.e. overdriven)
than the nominal values, thus producing more light but only
for a limited amount of time: in this case we say that the LED is
operated in strobed mode.
Strobing is needed whenever the application requires an
increased amount of light to freeze the motion of fast moving
objects, in order to eliminate the influence of ambient light, to
preserve the LED lifetime and to synchronize the ON time of your
light (ton) with the camera and item to be inspected.
To properly strobe an LED light, a few parameters must be
considered (Fig. 5 and 6):
• Max pulse width or ON time (ton max ): the maximum amount
of time for which the LED light can be switched on at the
maximum forward current.
• Duty cycle D is defined as (usually expressed in %):
D = ton/(ton+toff )
Where toff is the amount of time for which the LED light is off and
T = ton+toff is the cycle period. The duty cycle gives the fraction
in % of the cycle time during which the LEDs can be switched
on. The period T can be also given as the cycle frequency f = 1/T,
expressed in Hertz (Hz).
t on max
t off
Fig. 5: Duty cycles parameters.
Trigger signal
Trigger signal
Acquisition time
Acquisition time
t on
Strobed LED
light output
t on
t off
LED constant light output
Strobed LED
light output
Fig. 6: Triggering and strobing.
LED lifetime
he life of an LED is defined as the time that it takes for the LED luminance to decrease to 50% of its initial luminance at an ambient
temperature of 25°C.
Line speed, strobing and exposure time
hen dealing with online applications, there are some important parameters that have to be considered.
Specifically, depending on the object speed and image sharpness that is required for the application, the camera exposure time
must be always set to the minimum in order to freeze motion and avoid image blurring. Additionally, black and opaque objects that tend
to absorb instead of reflecting light, are particularly critical.
As an example, let’s suppose to inspect an object moving with speed vo using a lens with magnification m and a camera with pixel size p.
The speed of the object on the sensor will be m times vo:
vi = m vo,
Therefore the space travelled by the object xi during the exposure time t is xi = vi t. If this space is greater than the pixel size, the object
will appear blurred over a certain number of pixels. Suppose that we can accept a 3 pixels blur: in other words, we require that
xi = vi t = m vo t < 3 p
so that the camera exposure time t is required to be
t < 3 p / (m vo )
For example, using p = 5.5 µm, m = 0.66, vo = 300 mm/s (i.e. a line speed of 10,800 samples/hr on a 100 mm FoV) we find a maximum
exposure time of t = 83 µs.
At such speed, the amount of light emitted by LED illuminator used in continuous mode is hardly ever enough - so that strobing the
illuminator for an equivalent amount of time is the best solution.
Another parameter that we can adjust in order to get more light into the system is the lens F/#: by lowering the lens F/# we will gather
more light; however, this will lower the depth of field of the system. Moreover, this might also lower the image quality since, in general,
a lens performs better in the center and worse towards the edges due to lens aberrations, leading to an overall loss of sharpness.
Increasing the camera gain is another way, however this always introduces a certain amount of noise, thus again leading to a degraded
image where fewer details can be distinguished.
As a result, it is always a good practice to choose sufficiently bright lighting components, allowing you to correctly reveal the features
of interest the inspected of object when used in combination with lenses set at the optimum F/# and without the need to digitally
increase the camera gain.
Illumination geometries and techniques
ow to determine the best illumination for a specific machine vision task?
There are in fact several aspects that must be taken into account to help you choose the right illumination for your vision system
with a certain degree of confidence.
Application purpose
his is by far the first point that must be clear.
If we want to inspect the surface of an object to look for defects or features such as printed text, then front illumination is needed i.e. light coming from the camera side. Selecting the proper light direction or angle of incidence on the target surface as well as other
optical properties such as diffuse or direct light depends on the specific surface features that must be highlighted.
If, on the other side, we plan to measure the diameter or the length of an object or we want to locate a through-hole, the best choice
to maximize contrast at the edges is back illumination - i.e. light is blocked by the object on its way to the camera.
The choice is not so obvious when dealing with more complex situations such as transparent materials and sometimes mixed solutions
must be taken into account.
Illumination angle
Once we have established whether front or back illumination is more suitable, we must set the angle at which light hits the object
surface. Although the angle may vary, there are two important subgroups of front and backlight illumination: bright field and dark field
illumination. The four combinations that follow are described below (Fig. 7).
bright field
Front coaxial
and collimated illumination
bright field
dark field
dark field
dark field
bright field
Back coaxial
and collimated illumination
dark field
bright field
Fig. 7: Illumination and directionality: the ‘W rule’.
n bright field, front light illumination, light reflected by a flat
surface is collected by the optics.
This is the most common situation, in which non-flat features
(e.g. defects, scratches etc.) can scatter light outside the
maximum acceptance angle of the lens, showing dark
characteristics on a bright background (the bright field see Fig. 8 and 10.a 10.b).
Bright field, front light can be produced by LED barlights or
ringlights, depending on the system symmetry (Fig. 9).
In both cases LED light can be direct or diffused by a medium
(sometimes the latter is to prefer to avoid uneven illumination
on reflective surfaces).
Fig. 8: Front bright field illumination scheme.
Fig. 9: Ringlight (a) and barlight (b) geometry.
Fig. 10. a: image of engraved sample with front
brigth field illumination (ringlight).
Fig. 10.b: image of a metal coin (featuring embossed
parts) with front bright field illumination (ringlight).
n dark field, front light illumination, reflected light is not
collected by the optics. In this way, only scattered light is
captured, enhancing the non-planar features of the surface as
brighter characteristics on a dark background (the dark field see Fig. 11 and 13.a - 13.b ).
Again, this effect is commonly reproduced by means of low angle
ringlights (Fig. 12).
Fig. 11: Front dark field illumination scheme.
Fig. 12: Low angle ringlight geometry.
Fig. 13.a: image of engraved sample with front dark
field illumination (ringlight).
Fig .13.b: image of a metal coin (featuring embossed
parts) with front dark field illumination (ringlight).
n bright field, backlight illumination, light is either stopped
or transmitted by the surface if the material is opaque (Fig. 14)
or transparent.
In the first case, we see the outline of the object (black object on
white background - see Fig. 16 and 18).
In the latter, the non-planar features of the transparent object
show up dark on a white background; in this second case,
contrast is usually low unless the transparent surfaces present
sharp curvatures (e.g. air bubble inclusions in plastic).
These lighting techniques can be achieved using diffuse
backlights (Fig. 15a, 15b and 16) or telecentric illuminators,
specifically designed for high accuracy applications
(Fig. 17 and 18).
Fig. 14: Bright field backlight illumination scheme.
Fig. 15.a: Diffuse backlight geometry (back emitting).
Fig. 16: image of a plastic cap with backlight illumination.
Fig. 15.b: Diffuse backlight geometry (side-emitting).
Fig. 17: Telecentric backlight geometry.
Fig. 18: image of a precision mechanical component
with telecentric backlight illumination.
n dark field, backlight illumination, only light transmitted by
the sample and scattered by non-flat features will be collected,
enhancing such features as bright on the dark background
(Fig. 19).
This can be obtained by means of ringlights or bar lights
positioned behind a transparent sample.
Fig. 19: Dark field back light illumination scheme.
oaxial illumination. When front light hits the object surface
perpendicular to the object plane, we speak of coaxial
Coaxial illumination can additionally be collimated, i.e. rays are
parallel to the optical axis (within a certain degree).
To obtain this illumination set up, coaxial boxes are available
for use in combination with any type of lens (either fixed focal,
macro or telecentric) or telecentric lenses with built-in coaxial
illumination can be used (such as Opto Engineering® TCCX series).
The difference lies in the degree of collimation which results in
the amount of contrast that is possible to achieve searching for
defects on highly reflective surfaces. See Fig. 21 and 22.
Fig. 20: Coaxial illumination scheme (non collimated).
Fig. 21: Coaxial illumination geometry
(standard and collimated).
Fig. 22: image of engraved sample
with coaxial illumination.
ome lights and tunnel lights.
If an object with a complex curved geometry must be
inspected to detect specific surface features, front light
illumination coming from different angles is the most appropriate
choice in order to get rid of reflections that can lead to uneven
illumination: Dome lights are the ideal solution for these type of
applications because they are designed to provide illumination
coming from virtually any direction (Fig. 23 e 24).
Fig. 23: Dome illumination geometry.
In fact, dome lights are sometimes also referred to as “cloudy day”
illuminators because they provide uniform light as on a cloudy
Another type of lighting geometry is tunnel illumination: these
lights are designed to provide uniform illumination on long and
thin cylindrical objects and they feature a circular aperture on
top (as dome lights).
Fig. 24: Image of a metal coin (featuring embossed parts) with dome
light illumiantion.
ombined and advanced illumination solutions. Sometimes
in order to inspect very complex object geometries it is
necessary to combine different types of lights to effectively
reveal surface defects.
For example, the combination of a dome and a low angle light is
very effective in providing uniform illumination over the entire
field of view.
An example of “combined” lighting is the Opto Engineering ®
LTDMLA series, featuring all-in-one dome and low angle ring
lights which can be operated simultaneously or independently of
each other (see Fig. 25).
Fig. 25: Combined light (dome + low angle ringlight) illumination geometry.
Telecentric illumination
elecentric illumination is needed in a wide variety of applications
• High speed inspection and sorting: in fact, when coupled
with a telecentric lens, the high throughput allows for extremely
short exposure times
• Silhouette imaging for accurate edge detection and defect analysis
• Measurement of reflective cylindrical objects: diffuse backlights
can generate undesired reflections from the edges of shiny round
objects, making them look smaller than they are and leading
to inaccurate measurements. Since collimated rays are typically
much less reflected, telecentric illuminators can effectively
eliminate this “border effect” ensuring accurate and consistent
readings (see Fig. 26)
• Any precision measurement application where accuracy,
repeatability and high throughput are key factors
Non-collimated back illumination
Light coming from a variety of angles
Collimated back illumination
Parallel rays
Fig. 26: Collimated vs diffuse backlight illumination.
The use of a collimated light in combination with a telecentric lens
increases the natural depth of field of the telecentric lens itself
by approximately +20/30% (this however also depends on other
factors such as the lens type, light wavelength and pixel size).
Therefore, the optical system behaves as if the lens had the same
NA as the illuminator in terms of field depth, while maintaining
the same image resolution given by the actual telecentric lens
Additionally, thanks to the excellent light coupling, the distance
between the object and the light source can be increased where
needed without affecting image quality. This happens because the
illuminator’s numerical aperture (NA) is lower than the telecentric
lens NA.
Collimated light is the best choice if you need to inspect objects
with curved edges; for this reason, this illumination technique is
widely used in measurement systems for shafts, tubes, screws,
springs, o-rings and similar samples.
Wavelength and optical performance
any machine vision applications require a very specific light
wavelength that can be generated with quasi-monochromatic
light sources or with the aid of optical filters.
In the field of image processing, the choice of the proper light
wavelength is key to emphasize only certain colored features of
the object being imaged.
The relationship between wavelength (i.e. the light color) and the
object color is shown in Fig. 27. Using a wavelength that matches
the color of the feature of interest will highlight this specific
feature and viceversa, i.e. using opposite colors to darken non
relevant features (see Fig. 28).
For example green light makes green features appear brighter
on the image sensor while red light makes green features appear
darker on the sensor. On the other hand, white light will contrast
all colors, however this solution might be a compromise.
Additionally it must be considered that there is a big difference in
terms of sensitivity between the human eye and a CMOS or CCD
sensor. Therefore it is important to do an initial assessment
of the vision system to determine how it perceives the object,
in fact what human eyes see might be misleading.
Blue object
Red object
Monochromatic light can be obtained in two ways: we can
prevent extraneous wavelengths from reaching the sensor by
means of optical filters, or we can use monochromatic sources.
Optical filters allow only certain wavelengths of light to be
transmitted. They can be used either to allow light of a specified
wavelength to pass through (band-pass filters) or to block desired
wavelengths (e.g. low-pass filters for UV light only).
Color filters can block other non-monochromatic light sources
often present in industrial environments (e.g. sunlight, ceiling
lights etc.), however they also limit the amount of light that
actually reaches the sensor.
On the other hand, quasi-monochromatic sources only produce
light of a certain wavelength within a usually small bandwidth.
Either way, if we select monochromatic (e.g. green) light,
every non-green feature will appear dark grey or black on the
sensor, depending on the filter bandwidth and the color of the
feature. This gives us a simple way to enhance contrast by using
monochromatic light with respect to the use of white light
(Fig. 29 - 34).
White object
Black object
Fig. 27: Relationship between object color and light color.
Fig. 28: One way to maximize contrast is to select the light color
that is on the opposite side of the wheel of the feature color.
In such case, features will appear dark on the image sensor.
Additionally, in some cases a specific wavelength might
be preferred for other reasons: for example, Opto Engineering ®
telecentric lenses are usually optimized to work in the
visible range and they offer the best performance in terms
of telecentricity and distortion when used with green light.
Furthermore, green light is a good tradeoff between the
resolution limit (which improves with shorter wavelengths)
and the transmission characteristics of common glasses
(which in fact have low transmission at short wavelengths).
In cases where any wavelength will fit the application, one might
choose a specific LED color just based on cost considerations.
Blue filter
Red filter
Blue light is reflected off the blue circle,
but is absortbd by the red background.
Red light is reflected off the red background,
but is absorbed by the blue circle.
Fig. 29: Filtering and coloured samples: concept scheme and monochromatic result.
Fig. 30: Color camera.
Fig. 31: Mono camera.
Fig.33: Green filter.
Fig. 34: Blue filter.
Fig. 32: Red filter.
Polarizing filters consist of special materials characterized by a distinctive optical direction: all light oscillating in this direction passes
through, while the other components of the wave are suppressed.
Since light reflected by a surface is polarized in the direction parallel to the surface itself, such reflection can be significantly reduced
or blocked by means of two polarization filters - one on the light and one on the lens.
Polarizing filters are used to eliminate glare effects occurring when imaging reflective materials, such as glass, plastic etc.
Structured illumination
he projection of a light pattern on a surface can easily give
information on its 3D dimensional features (Fig. 35).
For example, if we observe a line projected from the vertical
direction with a camera looking from a known angle, we can
determine the height of the object where the line is projected.
This concept can be extended using various different patterns,
such as grids, crosses, dots etc.
Projected pattern
Seen pattern
Although both LED and laser sources are commonly used for
pattern projection, the latter present several disadvantages
(Fig. 36). The laser light profile of the line has a Gaussian shape,
being higher at the center and decreasing at the edges of the
Additionally, projecting a laser onto a surface produces the so
called “speckle effect”, i.e. an interference phenomenon that
causes loss of edge sharpness of the laser line, due to the high
coherent nature of the laser light.
With laser emitters the illumination decays both across the line
cross section and along the line width. Additionally, lines from
laser emitters show blurred edges and diffraction/speckle effects.
LED pattern projectors ensure thinner lines, sharper edges and
more homogeneous illumination than lasers.
With laser emitters the illumination decays both across the line
cross section and along the line width.
On the other hand, using LED light for structured illumination
will eliminate these issues. Opto Engineering ® LED pattern
projectors feature thinner lines, sharper edges and more
homogeneous illumination than lasers. Since light is produced by
a finite-size source, it can be stopped by a physical pattern with
the desired features, collected by a common lens and projected
on the surface.
Light intensity is constant through the projected pattern with no
visible speckle, since LED light is much less coherent than laser
light. Additionally, white light can be easily produced and used in
the projection process.
Fig. 35: Structured light technique.
Laser emitters lines are thicker and show blurred edges;
diffraction and speckle effects are also present.
Fig. 36: LASER vs LED in structured light illumination.
Illumination safety and class risks of LEDs
according to EN62471
EC/EN 62471 gives guidance for evaluating the photobiological
safety of lamps including incoherent broadband sources of
optical radiation such as LEDs (but excluding lasers) in the
wavelength range from 200 nm through 3000 nm.
According to EN 62471 light sources are classified into risk
groups according to their potential photobiological hazard.
Risk Group
No photobiological hazard
Group Ia
No photobiological hazard under normal behavioral limitations
Group II
Does not pose hazard due to aversion response to bright light
or thermal discomfort
Group III
Hazardous even for momentary exposure
camera is a remote sensing device that can capture and store or transmit images. Light is
collected and focused through an optical system on a sensitive surface (sensor) that converts
intensity and frequency of the electromagnetic radiation to information, through chemical or
electronic processes.
The simplest system of this kind consists of a dark room or box in which light enters only from
a small hole and is focused on the opposite wall, where it can be seen by the eye or captured on a
light sensitive material (i.e. photographic film). This imaging method, which dates back centuries,
is called ‘camera obscura’ (latin for ‘dark room’), and gave the name to modern cameras.
Fig. 1: Working principle of a camera obscura.
Fig. 2: Camera obscura “View of Hotel de Ville, Paris, France, 2015”
Photo by Abelardo Morell ©.
Camera technology has hugely improved in the last decades, since the development of Charge
Coupled Device (CCD) and, more recently, of CMOS technology. Previous standard systems, such
as vacuum tube cameras, have been discontinued.
The improvements in image resolution and acquisition speed obviously also improved the quality
and speed of machine vision cameras.
Camera types
Matrix and Line scan cameras
ameras used in machine vision applications can be divided in two groups: area scan cameras (also called matrix cameras) and line
scan cameras. The first are simpler and less technically demanding, while the latter are preferred in some situations where matrix
cameras are not suitable. Area scan cameras capture 2-D images using a certain number of active elements (pixels), while line scan
cameras sensors are characterized by a single array of pixels.
Sensor sizes and resolution
ensor sizes (or formats) are usually designated with an
imperial fraction value – i.e. 1/2”, 2/3”. However, the actual
dimensions of a sensor are different from the fraction value,
which often causes confusion among users. This practice dates
back to the 50’s at the time of TV camera tubes and is still the
standard these days. Also, it is always wise to check the sensor
specifications, since even two sensors with the same format
may have slightly different dimensions and aspect ratios. Spatial
resolution is the number of active elements (pixels) contained in
the sensor area: the higher the resolution, the smaller the detail
we can detect on the image.
Suppose we need to inspect a 30 x 40 mm FoV, looking for 40*40
μm defects that must be viewed on at least three pixels.
Sensor type
There can be 30*40/(0.04*0.04) = 0.75x10^6 defects. Assuming
a minimum of 3 pixels are required to see a defect, we need a
camera with at least 2.25 MP pixels. This gives the minimum
resolution required for the sensor, although the whole system
resolution (also including the lens resolution) must always be
assessed. Table 1 gives a brief overview of some common sensor
dimensions and resolutions. It is important to underline that
sensors can have the same dimensions but different resolution,
since the pixel size can vary. Although for a given sensor format
smaller pixels lead to higher resolution, smaller pixels are not
always ideal since they are less sensitive to light and generate
higher noise; also, the lens resolution and pixel size must always
be properly matched to ensure optimal system performances.
4 K (linear)
8 K (linear)
Sensor size
4.80 x 3.60
6.40 x 4.80
8.45 x 7.07
12.8 x 9.64
18.1 x 13.6
12 K (linear)
Pixel size
960 x 720
1280 x 960
1690 x 1414
2560 x 1928
3620 x 2720
0.6 M
1.2 M
2.5 M
10 M
12 K
Table 1: Examples of common sensor sizes and resolutions.
Sensor types: CCD and CMOS
he most popular sensor technologies for digital cameras are CCD and CMOS.
CCD (charged-couple device) sensors
consist of a complex electronic board
in which photosensitive semiconductor
elements convert photons (light) into
electrons. The charge accumulated is
proportional to the exposure time.
Frame transfer (FT)
Full frame (FF)
Interline (IL) =
Progressive scan
Light is collected in a potential well and
is then released and read out in different
ways (cf. Fig. 3). All architectures basically
shift the information to a register,
sometimes passing through a passive area
for storage.
The charge is then amplified to a voltage
signal that can be read and quantified.
Active & exposed pixel area
Passive area for storage and transfer
Register pixels for read-out
Fig. 3: CCD architectures.
CMOS (complementary metal-oxide semiconductor) sensors are conceptually different from CCD sensors, since the readout can be
done pixel by pixel rather than in sequential mode. In fact, signal is amplified at each pixel position, allowing you to achieve much higher
frame rates and to define custom regions of interest (ROIs) for the readout.
CMOS and CCD sensors were invented around the same time and, although historically CCD technology was regarded as superior, in
recent years CMOS sensors have caught up in terms of performance.
Global and rolling shutter (CMOS). In rolling shutter CMOS
sensors, the acquisition is progressive from the upper to the last
row of pixels, with up to 1/frame rate time difference between
the first and the last row.
Once the readout is complete, the progressive acquisition
process can start again. If the object is moving, the time
difference between pixels is clearly visible on the image, resulting
in distorted objects (see Fig. 4). Global shutter is the acquisition
method in which all pixels are activated simultaneously, thus
avoiding this issue.
Fig. 4: Rolling shutter effect.
Sensor and camera features
Sensor characteristics
ixel defects can be of three kinds: hot, warm and dead pixels. Hot pixels are elements that always saturate (give maximum signal, e.g.
full white) whichever the light intensity is. Dead pixels behave the opposite, always giving zero (black) signal. Warm pixels produce
random signal. These kinds of defects are independent of the intensity and exposure time, so they can be easily removed – e.g. by
digitally substituting them with the average value of the surrounding pixels.
Noise. There are several types of noise that can affect the actual pixel readout. They can be caused by either geometric, physical and
electronic factors, and they can be randomly distributed as well as constant. Some of them are presented below:
• Shot noise is a consequence of the discrete nature of light. When light intensity is very low - as it is considering the small surface
of a single pixel – the relative fluctuation of the number of photons in time will be significant, in the same way as the heads or tails
probability is significantly far from 50% when tossing a coin just a few times. This fluctuation is the shot noise.
• Dark current noise is caused by the electrons that can be randomly produced by thermal effect. The number of thermal electrons,
as well as the related noise, grows with temperature and exposure time.
• Quantization noise is related to the conversion of the continuous value of the original (analog) voltage value to the discrete value
of the processed (digital) voltage.
• Gain noise is caused by the difference in behavior of different pixels (in terms of sensitivity and gain). This is an example
of ‘constant noise’ that can be measured and eliminated.
Sensitivity is a parameter that quantifies how the sensor responds to light. Sensitivity is strictly connected to quantum efficiency, that
is the fraction of photons effectively converted into electrons.
Dynamic range is the ratio between the maximum and minimum
signal that is acquired by the sensor. At the upper limit,
pixels appear to be white for every higher value of intensity
(saturation), while pixels appear black at the lower limit and
The dynamic range is usually expressed by the logarithm of the
min-max ratio, either in base-10 (decibel) or base-2 (doublings
or stops), as shown in table 2. Human eyes, for example, can
distinguish objects both under starlight and on a bright sunny
day, corresponding to a 90 dB difference in intensity. This range,
though, cannot be used simultaneously, since the eye needs time
to adjust to different light conditions.
A good quality LCD has a dynamic range of around 1000:1, and
some of the latest CMOS sensors have measured dynamic ranges
of about 23 000:1 (reported as 14.5 stops).
10 000
1 000 000
1 073 741 824
10 000 000 000
Table 2: Dynamic range D, Decibels ( 10 log D ) and Stops ( log2 D ).
SNR (signal-to-noise ratio) considers the presence of noise, so that the theoretical lowest grey value as defined by the dynamic range is
often impossible to achieve. SNR is the ratio between the maximum signal and the overall noise, measured in dB. The maximum value
for SNR is limited by shot noise (that depends on the physical nature of light, and is this inevitable) and can be approximated as
SNRmax = sqrt [ maximum saturation capacity in electrons of a single pixel ]
SRN gives a limit on the grey levels that are meaningful in the conversion between the analog signal (continuous) and the digital one
(discrete). For example, if the maximum SNR is 50 dB, a good choice is a 8 bit sensor, in which the 256 grey levels corresponds to 48 dB.
Using a sensor with higher grey levels would mean registering a certain degree of pure noise.
Spectral sensitivity is the parameter describing how efficiently light intensity is registered at different wavelengths. Human eyes
have three different kinds of photoreceptors that differ in sensitivity to visible wavelengths, so that the overall sensitivity curve is the
combination of all three. Machine vision systems, usually based on CCD or CMOS cameras, detect light from 350 to 900 nm, with the
peak zone being between 400 and 650 nm. Different kinds of sensor can also cover the UV spectrum or, on the opposite side, near
infrared light, before going to drastically different technology for far wavelengths such as SWIR or LWIR.
EMVA Standard 1288
he different parameters that describe the characteristics and quality of a sensor are gathered and coherently described in the
EMVA standard 1288. This standard illustrates the fundamental parameters that must be given to fully describe the real behavior
of a sensor, together with the well-defined measurement methods to get these parameters.
The standard parameters are:
• Sensitivity, linearity of signal versus light intensity and noise
• Dark current (temperature dependence: optional)
• Sensor non-uniformity and defect pixels
• Spectral sensitivity (optional)
Measuring procedure
Sensitivity, linearity
and noise
Dark current
Sensor non-uniformity
and defect pixel
Spectral sensitivity
Test measuring amount of light at
increasing exposure time, from closed
shutter to saturation. Quantity of light
is measured (e.g. photometer)
Measured from dark images taken
at increasing exposure times. Since
dark current is temperature dependent,
behavior at different T can be given
A number of images are taken without light
(to see hot pixels) and at 50% saturation.
Parameters of spatial distortion
are calculated using Fourier algorithms
Quantum efficiency (photons converted
over total incoming photons ratio in %)
Dark and bright signal non-uniformity
Temporal dark noise, in electrons (e-)
Dark and bright spectrograms and (logarithmic)
Absolute sensitivity threshold (minimum
number of photons to generate a signal)
Dynamic range, in stops
Signal registered in absence of light,
in electrons per second
Images taken at different
Spectral sensitivity curve
SNR, in stops
Saturation capacity (maximum number of
electrons at saturation)
Camera Parameters
xposure time is the amount of time in which light is allowed to
reach the sensor. The higher this value, the higher the quantity
of light represented on the resulting image.
Increasing the exposure time is the first and easiest solution
when light is not enough but it is not free from issues: first, noise
always increases with the exposure time; also, blur effects can
appear when dealing with moving objects. In fact, if the exposure
time is too high, the object will be impressed on a number of
different pixels, causing the well-known ‘motion blur’ effect (see
Fig. 5).
On the opposite side, too long exposure times can lead to
overexposure – namely, when a number of pixels reach maximum
capacity and thus appear to be white, even if the light intensity on
each pixel is actually different
Fig. 5: Motion blur effect.
Frame rate. This is the frequency at which a complete image is captured by the sensor, usually expressed in frames per second (fps).
It is clear that the frame rate must be adjusted to the application: a line inspecting 1000 bottles per minute must be able to take images
with a minimum frame rate of 1000/60 = 17 fps.
Triggering. Most cameras give the possibility to control the beginning of the acquisition process, adjusting it to the application.
A typical triggering system is one in which light is activated together with the image acquisition after receiving an input from an
external device (e.g. position sensor).
This technique is essential when taking images of moving objects, in order to ensure that the features of interest are in the field of view
of the imaging system.
Gain in a digital camera represents the relationship between the number of electrons acquired and the analog-to-digital units (ADUs)
generated, i.e. the image signal. Increasing the gain means increasing the ratio between ADUs and electrons acquired, resulting in
an apparent higher brightness of the image. Obviously, this process increases the image noise as well, so that the overall SNR will be
Binning is the camera feature that combines the readout of
adjacent pixels on the sensor, usually in rows/columns, more
often in 2 x 2 or 4 x 4 squares (see Fig. 6).
Although resolution obviously decreases, there are a number
of other features improving. For example, with 2x2 binning,
resolution is halved, but sensitivity and dynamic range are
increased by a factor of 4 (since the capacitiec of each potential
well are summed), readout time is halved (frame rate doubled)
and noise is quartered.
Horizontal binging
Charges from
two adjacent pixels
in the line are summed
and reported out as
a single pixel.
Vertical binging
Full binging
Charges from
adjacent pixels in two lines
are summed and reported
out as a single pixel.
Charges from groups
of four pixels are summed
and reported out as
a single pixel.
Fig. 6: Sensor binning .
Digital camera interfaces
Camera Link
he Automated Imaging Association (AIA)
standard, commonly known as Camera
Link, is a standard for high-speed transmission
of digital video. AIA standard defines cable,
connector and camera functionality between
camera and frame grabber.
Speed. Camera Link offers very high performance in terms
of speed. It usually has different bandwidth configurations
available, e.g. 255 MB/s, 510 MB/s and 680 MB/s. The bandwidth
determines the ratio between image resolution and frame rate:
a typical basic-configuration camera can acquire 1 Mpixel image
at 50 frames/s or more; a full-configuration camera can acquire 4
Mpixel at more than 100 frames/s. Camera Link HS is the newer
standard that can reach 300 MB/s on a single line, and up to 6
GB/s on 20 lines.
Costs. Camera Link offers medium- to high-performance
acquisition, thus usually requiring more expensive cameras. Also,
this standard requires a frame grabber in order to manage the
hefty data load, not needed with other standards.
Cables. Camera Link standard defines a maximum length of 10 m
for the cables; one cable is needed for basic configuration, where
two are needed for full configuration cameras.
Power over cable. Camera Link offers a PoCL module (Power
over Camera Link) that provides power to the camera. Also,
several grabbers work with this feature.
CPU usage. Since Camera Link uses frame grabbers, which
transfer images to a computer as stand-alone modules, this
standard does not consume a lot of the system CPU.
Costs. In the simplest case, CoaXPress uses a single coaxial line
to transmit data, and coaxial cables are a simple and low-cost
solution. On the other hand, a frame grabber is needed, i.e. an
additional card must be installed, resulting in an additional cost
on the system.
Cables. Maximum cable length is 40 m at full bandwidth, or 100
m at half bandwidth.
Power over cable. Voltage supply provided goes up to 13 W at 24
V, that is enough for many cameras.
CPU usage. CoaXPress, just like Camera Link, uses frame
grabbers, which transfer images to computer as stand-alone
modules, i.e. this standard is very light on consuming the system
Cables. Cabling length is the keystone of GigE standard, going up
to 100 m. This is the only digital solution comparable to analog
visioning in terms of cable length, and this feature has helped
GigE Vision to replace analog e.g. in monitoring applications.
Power over cable. Power over Ethernet (PoE) is often available
on GigE cameras. Nevertheless, some Ethernet cards cannot
supply enough power, so that powered switch, hub, or a PoE
injector must be used.
CPU usage. CPU loads of a GigE system can be different
depending on drivers used. Filtered drivers are more generic
and easer to create and use, but operate on data packets at high
level, affecting the system CPU. Optimized drivers are specifically
written for a dedicated network interface card, that working at
lower lever affects poorly the system CPU load.
Cables. Passive USB 3.0 cable has a maximum length of about
7 meters, and active USB 3.0 cable can reach up to 50 m with
Power over cable. USB 3.0 offers power up to 4.5 W that allows
to get rid of a separate power cable.
CPU usage. USB 3.0 Vision permits image transfer directly into
PC memory, without CPU usage.
oaXPress is the second standard,
developed after Camera Link. It basically
consists in power, data and control for the
device sent through a coaxial cable.
Speed. A single cable can transmit up to
781.25 MB/s from the device to the frame grabber and 20 Mbit/s
of control data from the frame grabber to the remote device,
that is 5-6 times the GigE bandwidth. Some models can run also
at half speed (390.625 MB/s). At present, up to 4 cables can be
connected in parallel to the frame grabber, reaching a maximum
bandwidth of approx. 1800 MB/s.
ig-E Vision is a camera bus technology
that standardizes the Gigabit Ethernet,
adding a ‘plug and play’ behavior (such
as device discovery) to the latter. For its
relatively high bandwidth, long cable length
and diffused usage it is a good solution for
industrial applications.
Speed. Gigabit Ethernet has a theoretical maximum bandwidth
of 125 MB/s, that goes down to 100 MB/s when considering
practical limitations. This bandwidth is comparable to FireWire
standard and is second only to Camera Link.
Costs. System cost of GigE vision is moderate; cabling is cheap
and it doesn’t require a frame grabber.
USB 3.0
he USB (Universal Serial Bus) 3.0 standard
is the second revision of USB standard,
developed for computer communication.
Building on USB 2.0 standard, it provides a
higher bandwidth and up to 4.5 W of power.
Speed. While USB 2.0 goes up to 60 MB/s, USB 3.0 speed can
reach 400 MB/s, similar to the Camera Link standard used in
medium configuration.
Costs. USB cameras are usually low cost; also, no frame grabber
is required. For this reason, USB is the cheaper camera bus in the
GenIcam Standard
he GenICam standard (GENeric Interface
for CAMeras) is meant to provide a generic
software interface for all cameras, independently from cameras
hardware. Some of the new technology standard, anyway, are
based on GenICam (es. Camera Link HS, CoaXPress, USB3
GenICam standard purpose is to provide a ‘plug and play’ feature
for every image system. In consists in three modules that help
solving main tasks in machine vision filed in a generic way:
• GenApi: using a description file (XML), camera configuration
and access-control is possible
• Standard Feature Naming Convention (SFNC): these are
recommended names for common features in cameras
to reach the goal of interoperability
• GenTL: describes the transport layer interface
for enumerating cameras, grabbing images and transporting
them to the user interface
Vision systems
achine Vision is is the discipline that encompasses imaging technologies and methods
to perform automatic inspection and analysis in various applications, such as verification,
measurement, process control. A very common approach in machine vision is to provide turnkey
vision solutions, i.e. complete systems that can be rapidly and easily configured for use in the
field. A vision system is usually made up of every component needed to perform the intended
task, such as optics, lighting, cameras and software. When designing and building a vision system,
it is important to find the right balance between performance and cost to achieve the best result
for the desired application.
Usually vision systems are designed to work in on-line applications, where they have an
immediate impact on the manufacturing process (real-time systems). A classic example of this
on-line concept is the possibility to instantly reject a product deemed non-compliant:
the way this decision is made, as well as the object features being evaluated, defines different
classes of vision systems.
ision systems can do many different things: measurement, identification, sorting, code reading, character recognition, robot
guidance etc. They can easily interact with other machinery through different communication standards. Here below are some of
the main application categories for a vision system:
Measurement. One of the most important
uses of vision technology is to measure,
at various degrees of accuracy, the critical
dimensions of an object within predetermined tolerances.
Optics, lighting and cameras must be
coupled to effective software tools, since
only robust subpixeling algorithms will
allow to reach the accuracy often required
in measurement applications (e.g. even
down to 1 um).
Defect detection. Here various types of
product defects have to be detected for
cosmetic and/or safety reasons. Examples
of cosmetic flaws are stains, spots, color
clumps, scratches, tone variations, etc.
while other surface and/or structural
defects, such as cracks, dents, but also
print errors etc. can have more severe
Verification. The third major aim of a
vision system is checking that a product
has been correctly manufactured, in a
more general sense that goes beyond
what previously described; i.e. checking
the presence/absence of pills in a blister
pack, the correct placement of a seal or
the integrity of a printed label.
Vision systems
Types of vision systems
everal types of vision systems are available on the market, each being characterized by a different level of flexibility, performance
and cost. Vision systems can usually be divided into three classes: PC based, compact and smart camera based.
PC based. The classic machine vision system consists of an
industrial computer that manages and communicates with all
the peripheral devices, such as cameras and lighting, quickly
analyzing the information via software. This solution provides
high computing power and flexibility, but size and cost can be
significant. PC based systems are recommended for very complex
applications, where multiple inspection tasks must be carried out
at a fast rate with high-performance hardware.
Compact. A “lighter” version of a PC based system is called
a Compact vision system. Although it may require some tradeoff
between performance and cost, it is often enough for less
demanding applications. Compact vision systems usually include
a graphics card that acquires and transfers the information
to a separate peripheral (e.g. an industrial tablet or an external
monitor). Sometimes, compact vision systems not only manage
the first level input - lightning, camera and trigger inputs but also have embedded first level inputs.
Photo by Tim Coffey Photography. Source: Integro Technologies Corp.
Smart Cameras based. The simplest and most affordable vision systems are based on smart or intelligent cameras, normally used
in combination with standard optics (typically a fixed focal length lens) and lighting. Although typically recommended for simpler
applications, they are very easy to set up and provide similar functionalities to classic vision systems in a very compact form factor.
How a vision system works
he architecture of a vision system is strongly related to the application it is meant to solve. Some systems are “stand-alone”
machines designed to solve specific problems (e.g. measurement/identification), while others are integrated into a more complex
framework that can include e.g. mechanical actuators, sensors etc. Nevertheless, all vision systems operate are characterized by these
fundamental operations:
Image acquisition. The first and most important task of a vision system is to acquire an image, usually by means of light-sensitive
sensor. This image can be a traditional 2-D image, or a 3-D points set, or an image sequence. A number of parameters can be configured
in this phase, such as image triggering, camera exposure time, lens aperture, lighting geometry, and so on.
Feature extraction. In this phase, specific characteristics can be extrapolated from the image: lines, edges, angles, regions of interest
(ROIs), as well as more complex features, such as motion tracking, shapes and textures.
Detection/segmentation. at this point of the process, the system must decide which information previously collected will be passed on
up the chain for further elaboration.
High-level processing. The input at this point usually consists of a narrow set of data. The purpose of this last step can be to:
• Classify objects or object’s feature in a particular class
• Verify that the input has the specifications required by the model or class
• Measure/estimate/calculate specifics parameters as position or dimensions of object or object’s features
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